Tropical peatland in Southeast Asia has undergone rapid degradation and shows large subsidence due to oxidation and peat shrinkage. The measurement of those deformations is thus valuable for evaluating the peat condition and assessing peat restoration. The time series interferometric synthetic aperture radar (TInSAR), especially with the small baseline subsets (SBAS) method, is capable of measuring long-term deformation. However, the dynamic surface scatterers often change in tropical peatland, which degrades the coherent scatterer (CS) distribution density. This article presents a simple and efficient TInSAR approach that enhances the CS density under such dynamic surface scatter variation based on the SBAS method. In the presented approach, a long-time series of single-look complex images is separated into subsets, and deformation estimation is performed for each subset. The effectiveness of this simple solution was investigated by InSAR simulation and validated using SAR observation data. We applied the subset SBAS approach to the three-year Sentinel-1A C-band SAR dataset acquired over tropical peatland in Indonesia. The analyses showed an improved number of CSs for the introduced subset approach. We further introduce the color representation of CS temporal behavior per subset for visual interpretation of scatterer change.

Jakarta faces a critical challenge of extensive land subsidence, ranking prominently globally. This research employs a combined technical and policy evaluation approach to analyze the issue, incorporating sustainability considerations to assess the efficacy of Governor Regulation of Jakarta Number 93 of 2021, focusing on how the groundwater-free zone relates to land subsidence in the city. We processed 81 ALOS-2 PALSAR-2 synthetic aperture radar (SAR) data using persistent scatterer interferometric synthetic aperture radar (PS-InSAR) with HH polarization from 2017 to 2022 and ground truthing with 255 global positioning system (GPS) real-time kinematic (RTK) validation points. Our findings reveal a significant misalignment in the designated groundwater-free zone in the central part of Jakarta. At the same time, severe land subsidence primarily affects northern and northwestern Jakarta, with an average land subsidence rate of 5–6 cm/year. We strongly advocate for a thorough evaluation to rectify and redefine the boundaries of groundwater-free zones, improve regulatory frameworks, and effectively address land subsidence mitigation in the study area. The impact of domestic water needs on land subsidence highlights the urgency of action. Based on a combination of land subsidence velocity rates and domestic water demand, we have classified the cities in Jakarta into three levels of recommendations for groundwater-free zones. The cities are ranked in order of priority from highest to lowest: (1) West Jakarta, (2) North Jakarta, (3) South Jakarta, (4) East Jakarta, and (5) Central Jakarta, which holds the lowest priority.

The North Java coastal area, known as the Pantura region, is experiencing significant land subsidence, with certain areas sinking up to 10 cm per year. Pekalongan is among the most affected, with subsidence rates between 10 and 19 cm annually, mainly due to groundwater extraction, sediment compaction, and coastal erosion. Other coastal cities, like Semarang and Demak, show rates averaging 4 to 10 cm per year. This rapid subsidence is due to favorable geological conditions and ongoing urban development. This study investigates land subsidence in Pekalongan using the PS-InSAR method and dynamic visualization of time-series land cover data. PS-InSAR was applied to 45 scenes from ALOS-2 PALSAR-2 to monitor subsidence from 2014 to 2022. The results were validated with in situ subsidence benchmarks. Urban development dynamics were analyzed through land cover and land use change (LULC) and population density over the same period, using the GLC_FCS30D dataset in the GEE to detect non-natural LULC. The PS-InSAR results indicated that over 60.9% of investigation points experienced subsidence, up to 100 cm between 2014 and 2022. Ground validation showed an 83% agreement with PS-InSAR results. A statistical analysis of LULC from 2014 to 2022 did not show significant built-up area development, but the extension of salt marshes and water bodies indicated subsidence expansion. The population density reached 6873 people per square km by 2022, causing extensive groundwater use for domestic and industrial purposes, further aggravating the subsidence.

Forest fire in Indonesia occurs mostly in peatland area. Dry peatland areas with ground water table more than 40 cm from the soil surface have become degradation areas with high potentials to fire. This paper presents a new novel to detect a peat fire risk area by incorporating two methods; the impedance model and the differential interferometric SAR (DInSAR) technique which is based on the knowledge of annual subsidence rate associated with the groundwater table. The previous impedance model is modified in this study by integrating the surface roughness information in the model as apart of novelty. The proposed method was then validated with ground truth data of groundwater table. By using an impedance model, this research successfully detected peat fire risk area based on the backscattering coefficient simulation of dry peatland. Based on the simulation model, the average, minimum and maximum of backscattering coefficient of dry peat are -13.97 dB, -11.5 dB, and -17.29 dB, respectively. The correlation coefficient between the simulated backscattering coefficient and backscattering from ALOS-2/PALSAR-2 data is 0.8 with root mean square error of 1.4. By using DInSAR method, detection of dry peatland area was successful. The significant relationships are confirmed between groundwater table measurement and model are 0.71 for Pair A and 0.85 for Pair B. Both methods showed that peat fire risk areas were identified successfully. The dielectric constant of the peat soil also revealed that the soil condition of the area of interest is very dry indicating the potential to peat fire risk. Employing two models respectively were recommended to get precision of detection analysis.

Deforestation in peatland areas such as Kalimantan, Indonesia has been going on for decades. The deforestation has indirectly increased peatlands to become degraded and flammable. The Synthetic Aperture Radar (SAR) interferometry approach for identification of degraded peatlands can be performed using ALOS-2 PALSAR-2 data by converting land deformation data generated from SAR interferometry analysis into water table (WT) depth data using Wosten models. Peatlands with WT depth conditions of more than 40 cm are classified as degraded peatlands which are flammable. By using fire data from previous studies, this research confirms that identification of degraded peatlands using SAR interferometry approach by ALOS-2 PALSAR-2 is more reliable with high precision related to forest fires, with a precision level of 88% compared to 5% precision level using the WT depth monitoring system that has been installed in Central Kalimantan. The highest wavelength of ALOS-2 PALSAR-2 (L-Band) data can resolve the limitation due to temporal and volumetric decorrelation, compared to C-Band and X-Band satellite data. The combination methods of SAR interferometry approach and the real-time WT depth monitoring system to identify degraded peatlands can be more efficient, faster, and accurate. The advantage of this research result shows that SAR interferometry analysis can reach blank spot areas that are not covered by the observation station of WT depth monitoring system. It also gives a benefit as a guide to select precise locations of observation stations related to degraded peatland and forest fire.